Strengthening the Assessment of Long-Term Treatment Outcomes in MS
The gold standard for measuring treatment outcomes in multiple sclerosis (MS) is the randomized clinical trial (RCT). However, RCTs are often short-sighted and biased in their execution. Dr Maria Sormani, PhD, Biostatistics Unit, Department of Health Sciences, University of Genoa, Genoa, Italy, addressed this problem in a lecture on immunomodulatory treatment of MS. The presentation was part of a Hot Topic series at the 29th Congress of the European Committee for the Treatment and Research in Multiple Sclerosis (ECTRIMS). Dr Sormani shared findings on the inadequacy of randomized clinical trials (RCT), and highlighted the limitations of both observational studies and long-term follow ups as methods of assessing long-term effects of MS treatment.
The current standard for assessing treatment results in MS is the randomized clinical trial (RCT), which typically lasts 2 to 3 years. According to Sormani, however, this span of time does not account for the long-term effects of any given treatment. While the short-term efficacy of a treatment can be seen through MRI activity and clinical activity, long-term effectiveness can be manipulated by several interventions in the patient’s course of disease. Therefore, what RCTs really produce is the effect of early versus delayed treatment start, rather than the effect of treatment versus placebo. One example taken from a BENEFIT study clinical trial examined subjects randomized to IFNbeta and found that if the long-term follow up had lasted an additional 4 years, results would have shown a 50% conversion to MS. It is imperative, then, that these long-term effects are assessed when conducting RCTs. Currently, there are 2 approaches to measuring long-term effect in MS: observational studies and long-term follow up of clinical trials.
According to Sormani, the use of observational studies, while effective, is hindered by several limitations. As observational studies seek to compare treated to untreated cohorts, those comparisons are distorted by such factors as selection bias, start-time of treatment, and immortal time bias. To remedy this, Sormani suggests adjusting for baseline covariates and careful selection of control groups made up of contemporary and historical controls.
To illustrate this point, Sormani used a clinical trial from the British Columbia database which integrated these 2 methods. One assessment of the trial, which compared different reactions to interferon beta exposure among treated and untreated patients, presented opposite outcomes between the contemporary, untreated cohort, and the historical cohort. This difference between cohorts shows that, as Sormani puts it, “these comparisons are very hard to be interpreted, because if choosing a different control group, you can have another, different result.”
The second approach to long-term assessment is the long-term follow up of clinical trials. While observational studies are limited by biases, according to Sormani, long-term follow-up results are limited by an unclear distinction between early and delayed treatment start, and drop-out rates. Sormani explained that in clinical trials, patients are likely to drop out if they feel they are doing poorly which would then produce biased results in a long-term follow-up study. Again, Sormani pointed to a clinical trial in which 28 patients, who had died during the course of the trial, were excluded from follow-up study because of a lack of EDSS data. “If you include these patients as events in the comparison,” Sormani said, “the difference becomes in favor of treatment.” Dr Sormani concluded her presentation with a call for more well-designed and carefully analyzed observational studies and long-term follow ups of clinical trials in MS.